1. Field of the Invention
This invention relates generally to data analysis and more particularly to a system and method for analyzing and aggregating an unlimited amount of data and delivering customizable reports generally used for business intelligence purposes.
2. Description of the Related Art
Vast amounts of data are available for corporations concerning their customers. A business has numerous contact points with customers including but not limited to the Internet, Interactive Voice Response (IVR) systems, private company databases, and Enterprise Resource Planning (ERP) systems. Each of these customer contact points or data sources contain data capable of being mined for business intelligence purposes.
The Internet has allowed unlimited access for customers to a company's web site. This unlimited access creates a wealth of information in the form of web log files. Companies can use the web log files to extract information concerning the customers use of the web site.
An organization may have a phone system capable of Interactive Voice Response that customers routinely access. Data stored in the IVR data files can be presented to provide a profile of a customer's use of the IVR system. In addition, private company databases also contain data files that can be mined for business intelligence purposes.
Typically a business will have in-house databases containing accounting, financial and sales data. These in-house databases are commonly referred to as ERP systems and are a valuable source of financial data.
It can be appreciated that there are a large number of sources containing data that can be aggregated by businesses to analyze customer interactions. The data contained in each of the above referenced data sources consists of various data formats. Under current practices, analyzing and generating meaningful reports from the various data sources is an expensive and time-consuming process.
In order to effectively extract and organize the data, a company often times requires professional data handlers such as system administrators, database administrators, programmers and business analysts to analyze each data file, to understand the data type and to organize the relevant information. The professional data handlers then take the relevant data and place it in a uniform format (e.g., tables database formats, spreadsheet formats, etc.), thereby generating a static report summarizing the information associated with the business parameters of interest and customer interaction with various forms of data. For a large enterprise this effort can take teams of professionals whose sole focus is to routinely analyze this data and continually format the data to generate the static reports. For example, FIG. 1 displays a flowchart of the current process employed by industry. The various data files (DF1, DF2, DF3, and DFn) represent the vast amount of data available to an organization. Typical data files include web log files 102a, phone system data 102b, private company databases 102c and in-house operations data 102n. As described above, the data contained in each of the data files are of various formats. Each of the data files must be looked at to understand the data type and organize the data 104a, 104b, 104c and 104n. This is performed manually by professional data handlers 106. The professional data handlers 106 manipulate the data so that the data can be presented in a static report 108 that is dated.
In addition, the manual data handling described above places the information extracted from each individual data file into a separate database, each database having a common format. Therefore, retrieval of the information is from a number of databases and not from a central location, thereby resulting in inefficiencies in manipulating, storing, and then presenting the requested data. It is also important to note that such pre-processing of the data can take so much time that the data that is finally presented is actually dated. For this reason alone, the resultant data is oftentimes rendered useless for making time sensitive decisions regarding important business actions in response to customer activity. Much of the aforementioned problems with data manipulation and business intelligence services arise because of the lack of any software capable of extracting, organizing and uniformly formatting the required data from the various customer contact points, without excessive human interaction by computer programmers and other professionals. Moreover, report updates require the entire process to be repeated, thereby incurring a high cost and not allowing for the generation of true on demand reports. Accordingly, these services tend to be limited to large organizations that can afford the costs of processing the data from the various data sources.
As a result, there is a need for a solution to solve the problems of the prior art to effectively extract, organize and uniformly format the customer interaction data from various customer contact points and other business data sources. There is also a need for methods that enable accurate, efficient and timely presentation of the uniformly formatted data in the form of user requested reports.